CN116628283A - Manual data verification method based on big data - Google Patents
Manual data verification method based on big data Download PDFInfo
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Abstract
The invention discloses a manual data verification method based on big data in the field of data verification, which can divide standard data and entered data after manual entry into a plurality of first data subsets and second data subsets respectively, and inquire the first data subsets and the second data subsets respectively.
Description
Technical Field
The invention relates to the field of data verification, in particular to a manual data verification method based on big data.
Background
In the customs inspection process, enterprises need to provide own electronic accounts for customs departments, various influences are caused in the process of uploading electronic account data, and errors can be caused in the data due to the influence. Therefore, the uploaded data needs to be checked and compared, however, although the traditional data comparison method has simpler comparison logic, the comparison and operation speed are slower, and the comparison efficiency is not high, and the specific measure is that one piece of comparison data is extracted from the comparison data, and then the comparison data is compared with the compared data one by one and fed back to the comparison result one by one, and the comparison method has extremely huge operand and low comparison operation efficiency.
Disclosure of Invention
Technical problem to be solved
Aiming at the problems in the prior art, the invention provides a manual data verification method based on big data.
Technical proposal
The invention is realized by the following technical scheme:
a manual data verification method based on big data comprises the following steps: s1: acquiring manual data and defining the manual data as a first data set, acquiring input data of the manual, defining the input data as a second data set, wherein the sequence positions of the data in the first data set and the second data set are the same in one-to-one correspondence; splitting manual data and entered data into a plurality of first data subsets and second data subsets respectively; by adopting a splitting and inquiring mode, the area with unequal data before and after the manual input can be quickly positioned, and then the reasons of the unequal data can be further inquired.
S2: splitting data in a first data set into a plurality of first data subsets according to a fixed length, and splitting data in a second data set into a plurality of second data subsets according to a fixed length;
s3: respectively inquiring the first data subset and the second data subset to obtain a first data feedback subset and a second data feedback subset which are obtained in response to the search command;
s4: the first data feedback subset and the second data feedback subset are equal in number of target data obtained by responding to the search command, and the next first data feedback subset and the second data feedback subset are sequentially inquired; the method specifically comprises the steps that when target data in a first data feedback subset and target data in a second data feedback subset obtained by corresponding search commands are equal, the probability of occurrence of problems in the first data subset and the second data subset is smaller, and therefore the next first data subset and the next second data subset are queried sequentially;
s5: if the number of target data obtained by the response search command of the first data feedback subset is not equal to that of target data obtained by the response search command of the second data feedback subset, the first data subset and the second data subset are extracted through the data extraction module, and then the data in the first data subset and the second data subset are compared through the data comparison module; the method specifically comprises the steps of comparing the first data subset and the second data subset of the part preferentially to acquire the reasons of unequal target data when the target data in the first data feedback subset and the second data feedback subset obtained by corresponding search commands are unequal.
Further, the data sequence of the plurality of first data subsets is spliced and arranged and then is the same as the data sequence in the first data set.
Further, the data sequence of the plurality of second data subsets is spliced and arranged in the same order as the data sequence of the second data sets.
Further, in S5, the target data amounts obtained by the response search command of the first data feedback subset and the second data feedback subset are equal, and then the data in the first data subset and the second data subset are sequentially compared by the data comparison module.
Further, in S5, the data comparison module marks data corresponding to inconsistencies in the first subset of data and the second subset of data.
Further, the data, corresponding to the inconsistent data in the first data subsets and the second data subsets, are uniformly extracted by the data extraction module and are collected into a list for display.
Advantageous effects
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
the invention provides a manual data verification method based on big data, which can divide manual data and entered data into a plurality of first data subsets and second data subsets respectively, and inquire the first data subsets and the second data subsets respectively, when target data in the first data subsets and the second data subsets obtained by corresponding search commands are equal, the probability of occurrence of problems in the first data subsets and the second data subsets is smaller, so that the next first data subsets and the second data subsets are inquired sequentially, when target data in the first data subsets and the second data subsets obtained by corresponding search commands are not equal, the first data subsets and the second data subsets of the part are preferentially compared, and the reasons of the unequal target data are obtained. The invention adopts a splitting and inquiring mode, can rapidly locate the area of unequal data before and after the manual input, then further inquire the reasons of the unequal data, can rapidly find the phenomenon of data loss and locate the specific area, and has high verification efficiency and high detection speed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a unitary frame diagram of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Embodiment 1, in combination with fig. 1, a manual data verification method based on big data includes the steps of: s1: acquiring manual data and defining the manual data as a first data set, acquiring input data of the manual, defining the input data as a second data set, wherein the sequence positions of the data in the first data set and the second data set are the same in one-to-one correspondence;
s2: splitting data in a first data set into a plurality of first data subsets according to a fixed length, and splitting data in a second data set into a plurality of second data subsets according to a fixed length;
s3: respectively inquiring the first data subset and the second data subset to obtain a first data feedback subset and a second data feedback subset which are obtained in response to the search command;
s4: the first data feedback subset and the second data feedback subset are equal in number of target data obtained by responding to the search command, and the next first data feedback subset and the second data feedback subset are sequentially inquired;
s5: and if the target data quantity obtained by the response search command of the first data feedback subset and the second data feedback subset is unequal, the first data subset and the second data subset are extracted through the data extraction module, and then the data in the first data subset and the second data subset are compared through the data comparison module.
The invention provides a manual data verification method based on big data, which can divide manual data and entered data into a plurality of first data subsets and second data subsets respectively, and inquire the first data subsets and the second data subsets respectively, when target data in the first data subsets and the second data subsets obtained by corresponding search commands are equal, the probability of occurrence of problems in the first data subsets and the second data subsets is smaller, so that the next first data subsets and the second data subsets are inquired sequentially, when target data in the first data subsets and the second data subsets obtained by corresponding search commands are not equal, the first data subsets and the second data subsets of the part are preferentially compared, and the reasons of the unequal target data are obtained. The invention adopts a splitting and inquiring mode, can rapidly locate the area of unequal data before and after the manual input, then further inquire the reasons of the unequal data, can rapidly find the phenomenon of data loss and locate the specific area, and has high verification efficiency and high detection speed.
Further, the data sequence of the plurality of first data subsets is spliced and arranged and then is the same as the data sequence in the first data set.
Further, the data sequence of the plurality of second data subsets is spliced and arranged in the same order as the data sequence of the second data sets.
Further, in S5, the target data amounts obtained by the response search command of the first data feedback subset and the second data feedback subset are equal, and then the data in the first data subset and the second data subset are sequentially compared by the data comparison module.
Further, in S5, the data comparison module marks data corresponding to inconsistencies in the first subset of data and the second subset of data. The data which are inconsistent in correspondence among the plurality of first data subsets and the plurality of second data subsets are uniformly extracted by the data extraction module and are collected into a list for display, so that the data difference before and after manual data entry can be displayed clearly.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. A manual data verification method based on big data is characterized by comprising the following steps: the method comprises the following steps: s1: acquiring manual data and defining the manual data as a first data set, acquiring input data of the manual, defining the input data as a second data set, wherein the sequence positions of the data in the first data set and the second data set are the same in one-to-one correspondence; splitting manual data and entered data into a plurality of first data subsets and second data subsets respectively; by adopting a splitting and inquiring mode, the area with unequal data before and after the manual input can be quickly positioned, and then the reasons of the unequal data can be further inquired.
S2: splitting data in a first data set into a plurality of first data subsets according to a fixed length, and splitting data in a second data set into a plurality of second data subsets according to a fixed length;
s3: respectively inquiring the first data subset and the second data subset to obtain a first data feedback subset and a second data feedback subset which are obtained in response to the search command;
s4: the first data feedback subset and the second data feedback subset are equal in number of target data obtained by responding to the search command, and the next first data feedback subset and the second data feedback subset are sequentially inquired; the method specifically comprises the steps that when target data in a first data feedback subset and target data in a second data feedback subset obtained by corresponding search commands are equal, the probability of occurrence of problems in the first data subset and the second data subset is smaller, and therefore the next first data subset and the next second data subset are queried sequentially;
s5: if the number of target data obtained by the response search command of the first data feedback subset is not equal to that of target data obtained by the response search command of the second data feedback subset, the first data subset and the second data subset are extracted through the data extraction module, and then the data in the first data subset and the second data subset are compared through the data comparison module; the method specifically comprises the steps of comparing the first data subset and the second data subset of the part preferentially to acquire the reasons of unequal target data when the target data in the first data feedback subset and the second data feedback subset obtained by corresponding search commands are unequal.
2. The big data-based manual data verification method according to claim 1, wherein: the data sequence of the plurality of first data subsets is spliced and arranged and then is the same as the data arrangement sequence in the first data set.
3. The big data-based manual data verification method according to claim 1, wherein: the data sequence of the plurality of second data subsets is spliced and arranged and then is the same as the data arrangement sequence in the second data set.
4. The big data-based manual data verification method according to claim 1, wherein: in S5, the number of target data obtained by the first data feedback subset and the second data feedback subset in response to the search command is equal, and then the data in the first data subset and the second data subset are sequentially compared by the data comparison module.
5. The big data-based manual data verification method according to claim 1, wherein: in S5, the data comparison module marks data corresponding to inconsistencies in the first subset of data and the second subset of data.
6. The big data based manual data verification method according to claim 5, wherein: and uniformly extracting and converging the data which are inconsistent in correspondence among the plurality of first data subsets and the plurality of second data subsets into a list for display by adopting a data extraction module.
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| CN202310722078.3A CN116628283A (en) | 2019-06-04 | 2019-06-04 | Manual data verification method based on big data |
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| CN202310722078.3A CN116628283A (en) | 2019-06-04 | 2019-06-04 | Manual data verification method based on big data |
| CN201910482758.6A CN110362722A (en) | 2019-06-04 | 2019-06-04 | A kind of handbook data method of calibration based on big data |
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| WO2010006416A1 (en) * | 2008-06-30 | 2010-01-21 | Ali Davar | System and method for interacting with a plurality of search engines |
| CN101639849B (en) * | 2009-06-01 | 2011-06-01 | 北京四维图新科技股份有限公司 | Method for processing data and device therefor |
| CN104298736B (en) * | 2014-09-30 | 2017-10-17 | 华为软件技术有限公司 | Data acquisition system connection method, device and Database Systems |
| CN107291716B (en) * | 2016-03-30 | 2020-07-21 | 阿里巴巴集团控股有限公司 | Link data checking method and device |
| CN108268496A (en) * | 2016-12-30 | 2018-07-10 | 北京国双科技有限公司 | The method of calibration and device of data |
| CN109344151A (en) * | 2018-08-08 | 2019-02-15 | 航天信息股份有限公司 | The method of batch registration and modification user information |
| CN109308299B (en) * | 2018-09-12 | 2020-01-14 | 北京字节跳动网络技术有限公司 | Method and apparatus for searching information |
| CN109635256B (en) * | 2018-12-20 | 2023-07-11 | 上海掌门科技有限公司 | Method and device for verifying data |
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